Browsing by Author "Manoj Kumar Singh"

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A Comparative Study of Feature Extraction Techniques and Similarity Measures for Image Retrieval
(Institute of Electrical and Electronics Engineers Inc., 2022) Mona Singh; Suneel Kumar; Ruchilekha; Manoj Kumar Singh
With the growing popularity of using massive amount image database in several applications, it is critical to develop an autonomous and efficient retrieval system to search the relevant images from entire database. The method of obtaining the relevant images from huge image libraries by extracting their content features is known as content-based image retrieval (CBIR). In this paper, a comparative study is performed while acquiring various methods of traditional feature extraction, such as Color moment, Gabor wavelet, Discrete wavelet transform (DWT), Local binary pattern (LBP), Gray level co-occurrence matrix (GLCM), and Histogram of orientation (HOG), to present an efficient and more accurate CBIR system. The experiment is demonstrated on two benchmark datasets, namely Wang (color images) and Medical MNIST (grayscale images), with different visual effects. To retrieve relevant images of a query image, three distinct distance metrics, such as Cosine, City block, and Euclidean, are used to examine the similarity between the query image and the database images. The experiment is evaluated using two performance metrics: precision and recall, to compare the efficacy of various approach. We achieve the best results as average precision of 65.65% and average recall of 6.57 on a scale of 10 using Color moment features via Euclidean distance metric in case of WANG dataset, while 99.89% and 9.99 on a scale of 10 for average precision and average recall using HOG features via City block distance metric in case of Medical MNIST dataset. © 2022 IEEE.
A deep learning approach for subject-dependent & subject-independent emotion recognition using brain signals with dimensional emotion model
(Elsevier Ltd, 2023) Ruchilekha; Manoj Kumar Singh; Mona Singh
This paper aims to design a deep-learning based approach in combination with machine learning classifiers for two different perspectives. In first perspective, the performance is evaluated when training and testing are performed on same subject called as subject–dependent evaluation criteria. In second perspective, the performance is evaluated when training and testing are performed on different subjects called as subject–independent evaluation criteria. For each perspective, three label cases are made using valence, arousal, and dominance for recognizing human emotions: i) Binary/ 2-class, ii) Quad/ 4-class, and iii) Octal/ 8-class classifications. The experiment is performed on two publicly available datasets DEAP and DREAMER. For emotion recognition, firstly the brain signals are processed and then features are extracted using our proposed deep convolutional neural network (DCNN) architecture. These extracted features are used for emotion recognition using classifiers namely Naive Bayes (NB), decision tree (DT), k-Nearest Neighborhood (KNN), Support Vector Machine (SVM), AdaBoost (AB), Random Forest (RF), Neural Networks (NN), Long-short term memory (LSTM), and Bidirectional-LSTM (BiLSTM). The experimental results give more robust classification for subject-independent emotion recognition in comparison to subject-dependent emotion recognition, with DCNN + NN for binary and DCNN + SVM for quad & octal classification. Moreover, experimental results show that arousal and dominance play an important role in emotion recognition in contrary to valence and arousal as reported in literature. © 2023 Elsevier Ltd
A derivative free globally convergent method and its deformations
(Springer Science and Business Media Deutschland GmbH, 2021) Manoj Kumar Singh; Arvind K. Singh
The motive of the present work is to introduce and investigate the quadratically convergent Newton’s like method for solving the non-linear equations. We have studied some new properties of a Newton’s like method with examples and obtained a derivative-free globally convergent Newton’s like method using forward difference operator and bisection method. Finally, we have used various numerical test functions along with their fractal patterns to show the utility of the proposed method. These patterns support the numerical results and explain the compactness regarding the convergence, divergence and stability of the methods to different roots. © 2021, The Author(s).
A hybrid algorithm with modified Inver-over operator and genetic algorithm search for traveling salesman problem
(Springer Verlag, 2016) Dharm Raj Singh; Manoj Kumar Singh; Tarkeshwar Singh
In this article, we develop a novel hybrid approach to solve the traveling salesman problem (TSP). In this approach, we first initialize suboptimal solution using Nearest Neighbor (NN) tour construction method, followed modified Inver-over operator and then proposed crossover with 2-opt mutation applied to improve for optimal solution. We use 14 TSP data sets from TSPLIB to evaluate the performance of proposed hybrid method. The proposed hybrid method gives better results in terms of best and average error. In experimental results of the tests we show that the proposed hybrid method is superior to available algorithm in literature. © Springer Science+Business Media Singapore 2016.
A hybrid heuristic algorithm for the Euclidean traveling salesman problem
(Institute of Electrical and Electronics Engineers Inc., 2015) Dharm Raj Singh; Manoj Kumar Singh; Tarkeshwar Singh
In this paper, we propose hybrid algorithm, 2-opt optimal (2-opt) heuristic mutation with nearest neighbor (NN) tour construction, for solving traveling salesman problem (TSP). In this method, we first initialize suboptimal solution with the help of NN tour construction then DPX crossover is being used and after that 2-opt heuristic method is applied to refine solution for global optimality. Standard benchmark data from TSPLIB is used to evaluate the performance of proposed algorithm. The proposed algorithm gives better performance in term of best and average error. The proposed algorithm decreases the best error values in comparison to other methods with the ratio in between 19.13% and 90.55% and average error values between 32.16% and 86.10%. © 2015 IEEE.
Agronomic aspects of zinc biofortification in rice (Oryza sativa l.)
(Springer India, 2014) Manoj Kumar Singh; Saroj Kumar Prasad
Globally, 2.7 billion people suffer from Zn deficiency (ZnD) and 1/3 of the world population living in the poor countries is at the high risk of this deficiency. A staggering number of ZnD deaths occur in South Asia alone. Though the causes of malnutrition are many and complex, one such cause is the dysfunctional food system which is dependent on agriculture. Rice is a staple food for 1/2-2/3 of the world's population and is mainly (90 %) grown in south, southeast and east-Asia. Nearly 50 % of the Indian soil contains inadequate Zn levels and this ZnD in rice-wheat system affects 50 % of rice, particularly, grown under lowland conditions. In order to address the ZnD issue in rice, various agronomic approaches of Zn biofortification can be tested, i.e., selection of cultivars, rate and time of Zn fertilizer application, crop rotation and use of soil microorganisms. Agronomic Zn biofortification is a promising and cost effective method to increase Zn concentration in rice grains. Thus it can save the life of millions of people in Asia, particularly in India. The present article is a modest attempt to analyze the viability of agronomic biofortification in rice grains as a short term and profitable tool to promote Zn concentration that would consequently cure several health hazards commonly visible among humans in the developing countries. © 2014 The National Academy of Sciences, India.
An Effective Deep Learning Model for Content-Based Gastric Image Retrieval
(Institute of Electrical and Electronics Engineers Inc., 2023) Mona Singh; Manoj Kumar Singh
In this paper, we propose a feature combination, also known as feature fusion, for improving performance in content-based gastric image retrieval (CBGIR). This study provides a CBGIR system that retrieves images by combining ResNet-18 and ResNet-50 information and finally, the Euclidean distance metric is evaluated for similarity measurement. The proposed approach is also compared to different deep learning techniques such as AlexNet, VGGs (VGG-16 & VGG-19), GoogleNet, SqueezeNet, DarkNet-19 models. The proposed method was examined on the KVASIR database with 4000 images and S different classes. We get the optimum results as average precision of 95.44% and average recall of 19.09 on a scale of 20 using the proposed deep learning model and Euclidean distance metric. . © 2023 IEEE.
An Efficient Hybrid Algorithm with Novel Inver-over Operator and Ant Colony Optimization for Traveling Salesman Problem
(Springer Science and Business Media Deutschland GmbH, 2024) Dharm Raj Singh; Manoj Kumar Singh; Sachchida Nand Chaurasia
In this research paper, we present a hybrid algorithm that merges the principles of Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Our algorithm consists of two distinct stages. In the first stage, we employ Ant Colony Optimization to establish an initial population, and we utilize the proposed Inver-over (IO) heuristic to obtain suboptimal solutions for the Euclidean Traveling Salesman Problem (TSP). The proposed Inver-over operator is used to refine the solution obtained through ACO. Subsequently, this refined solution is incorporated into the Genetic Algorithm (GA) for the second stage. In the second stage of our algorithm, we apply GA with our proposed crossover operator and a 2-optimal heuristic to further refine the solution with the goal of achieving global optimality. To assess the effectiveness of our proposed algorithm, we rely on standard benchmark data from TSPLIB. The experimental results indicate that our hybrid algorithm outperforms recent methods and exhibits greater efficiency when compared to other reported methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
An Efficient Hybrid Threshold for Image Deconvolution in Expectation Maximization Framework
(Birkhauser, 2024) Ravi Pratap Singh; Manoj Kumar Singh
In this paper, we present a computationally efficient hybrid thresholding method for image deconvolution in the expectation maximization (EM) framework. The proposed method alternates between two key steps: an E-step that exploits the fast Fourier transform (FFT) for inversion of the convolution operator and an M-step that uses the discrete wavelet transform (DWT) for denoising. A modified L1-clipped penalty is introduced in the M-step for the computation of the maximum a posteriori (MAP) estimate, leading to a hybrid thresholding scheme, which is a combination of both hard and soft thresholds. Hard thresholding gives high variance, while soft thresholding leads to high bias in the restored image. The proposed hybrid threshold ameliorates the bias and variance trade-offs of the hard and soft thresholding schemes. Also, we performed a detailed mathematical and statistical analysis of the proposed hybrid threshold and computed the risk. The experimental results show that the proposed method attains optimal values for both variance and bias, leading to minimum risk, and also outperforms the state-of-the-art methods by a significant margin in terms of the performance metrics peak signal-to-noise ratio (PSNR) and improved signal-to-noise ratio (ISNR), as well as the visual quality. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
An Efficient Hybrid Threshold for Image Deconvolution in Expectation Maximization Framework
(Birkhauser, 2025) Ravi Pratap Singh; Manoj Kumar Singh
In this paper, we present a computationally efficient hybrid thresholding method for image deconvolution in the expectation maximization (EM) framework. The proposed method alternates between two key steps: an E-step that exploits the fast Fourier transform (FFT) for inversion of the convolution operator and an M-step that uses the discrete wavelet transform (DWT) for denoising. A modified L1-clipped penalty is introduced in the M-step for the computation of the maximum a posteriori (MAP) estimate, leading to a hybrid thresholding scheme, which is a combination of both hard and soft thresholds. Hard thresholding gives high variance, while soft thresholding leads to high bias in the restored image. The proposed hybrid threshold ameliorates the bias and variance trade-offs of the hard and soft thresholding schemes. Also, we performed a detailed mathematical and statistical analysis of the proposed hybrid threshold and computed the risk. The experimental results show that the proposed method attains optimal values for both variance and bias, leading to minimum risk, and also outperforms the state-of-the-art methods by a significant margin in terms of the performance metrics peak signal-to-noise ratio (PSNR) and improved signal-to-noise ratio (ISNR), as well as the visual quality. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
An optimal 8th order Newton’s-type method with basin of attraction
(Springer Nature, 2022) Manoj Kumar Singh; Ioannis K. Argyros; Arvind K. Singh
The goal of the present work is to obtain an eighth order optimal Newton like method to solve non-linear algebraic equations. We also examine the dynamics of the proposed method. It is an optimal eighth order consistent with Kung–Traub conjecture with efficiency index 1.682. We have tested the proposed method using several numerical examples and also discussed the basin of attraction related to some numerical examples. © 2021, The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada.
Anti-inflammatory and hepatoprotective activities of the roots of Uraria picta
(BRNSS Publication Hub, 2017) Kritika Hem; Narendra Kumar Singh; Manoj Kumar Singh
Introduction: Uraria picta (Jacq.) commonly known as Prishnaparni, is one of the important ingredients of the 10 herb formulation called "Dashmula," used for the treatment of fever and inflammation. Materials and Methods: This study was conducted to investigate the anti-inflammatory and hepatoprotective activities of methanol extract of the roots of U. picta (UPME) at a dose of 100, 200 and 400 mg/kg, p.o. in experimental models of rats. Anti-inflammatory activity of methanol extract was performed by egg albumin-induced and formalin induced rats paw edema. Paracetamol (PCM)-induced liver injury model was used to explore the hepatoprotective activity of UPME. Results: Methanolic roots extract showed significant activity against both models of inflammation. UPME (400, 200, and 100 mg/kg, p.o.) reduced inflammation in egg albumin and formalin treated in dose-dependent manner. Administration of PCM 2000 mg/kg induced liver injury in rats, and therefore, increased the level of enzymes alanine transaminase (ALT), alkaline phosphatase (ALP), and aspartate aminotransferase (AST) in the blood. Administration of UPME 400, 200 and 100 decreased the level of enzymes ALT, ALP and AST significantly which were found comparable with the standard drug silymarin 100 mg/kg. Conclusion: This study demonstrated the ability of U. picta to exert anti-inflammatory and hepatoprotective effects.
ASSESSMENT OF AGRONOMIC ZINC BIOFORTIFICATION OF ALLEY CROPPED PEARL MILLET
(Bangladesh Botanical Society, 2023) Kamlesh Verma; Saroj Kumar Prasad; Manoj Kumar Singh; Prashant Sharma
The availability of nitrogen (N) and zinc (Zn) at the specific plant growth stage is crucial for attaining the higher nutrient use efficiency (NUE) and uptake. An experiment was conducted, having 4-N scheduling [No N; ½[basal]+ ½[3rd visible leaf (VL)]; ¼[basal]+ ½[3rdVL]+ ¼[panicle extended in flag leaf sheath (PEFLS)]; ½[basal] + ¼[3rdVL]+ ¼[panicle visible (PV)], and 4-Zn scheduling [No Zn; 2.5 kg/ha [basal]+ 0.25% spray(*) [panicle initiation (PI)]; 2.5 kg/ha [basal]+0.25% [PI]*+ 0.25% [PEFLS]*; 2.5 kg/ha [basal]+ 0.25% [50% panicle emergence (PE)]*+ 0.25% [milk stage (MS)]*. Nitrogen schedule at ¼[basal]+ ½[3rdVL]+ ¼[PEFLS] recorded the maximum nutrient content, uptake, and NUE. Similarly, the maximum nutrient content, uptake, and nutrient use efficiency observed in the Zn at 2.5 kg/ha [basal]+0.25% [PI]*+ 0.25% [PEFLS]*. Moreover, N and Zn interacted significantly to increase the grain N and Zn content and uptake by grain. © 2023 Bangladesh Botanical Society. All rights reserved.
Bifurcation analysis of modified Leslie-Gower predator-prey model with double Allee effect
(Ain Shams University, 2018) Manoj Kumar Singh; B.S. Bhadauria; Brajesh Kumar Singh
In the present article, a modified Leslie-Gower predator-prey model with double Allee effect, affecting the prey population, is proposed and analyzed. We have considered both strong and weak Allee effects separately. The equilibrium points of the system and their local stability have been studied. It is shown that the dynamics of the system are highly dependent upon the initial conditions. The local bifurcations (Hopf, saddle-node, Bogdanov-Takens) have been investigated by considering sufficient parameter(s) as the bifurcation parameter(s). The local existence of the limit cycle emerging through Hopf bifurcation and its stability is studied by means of the first Lyapunov coefficient. The numerical simulations have been done in support of the analytical findings. The result shows the emergence of homoclinic loop. The possible phase portraits and parametric diagrams have been depicted. © 2016 Ain Shams University
Biosurfactant producing microbes for clean-up of soil contaminants
(Elsevier, 2020) Sandeep Kumar Singh; Manoj Kumar Singh; Hariom Verma; Prem Pratap Singh; Anand Vikram Singh; Kumari Rashmi; Ajay Kumar
Microbial synthesized biosurfactants currently utilized in remediation of various organic pollutants including hydrocarbons, petroleum products, and oil spills and appear as latest and advanced approach in bioremediation. Biosurfactant is an amphipathic molecules constituted of both hydrophilic and hydrophobic groups, and their application reduced the surface or interfacial tensions of the immiscible fluids, which enhanced solubility and sorption potential of hydrophobic organic and inorganic compounds. Initially, chemically synthesized surfactants have been used for the remediation of hydrophobic contaminants but cost-effectiveness, toxic, and harmful residues limit their frequent use. In this regard, microbial synthesized biosurfactant emerges as a chief, eco-friendly, and best suitable alternative and frequently utilized in the environmental contamination management. In this chapter, we had summarized microbial source, limiting factors during biosurfactant production, and also discussed action mechanism against various environmental contamination. © 2021 Elsevier Inc.
Bulk-surface event discrimination in point contact germanium detectors at near-threshold energies with shape-matching pulse-shape methods
(Springer Nature, 2025) Jiashian Wang; Manoj Kumar Singh; Haubin Li; Henry Tsz King Wong
The p-type point-contact germanium (pPCGe) detectors have been widely adopted in searches for low energy physics events such as neutrinos and dark matter. This is due to their enhanced capabilities of background rejection, sensitivity at energies as low as the sub-keV range and particularly fine energy resolution. Nonetheless, the pPCGe is subject to irregular behaviour caused by surface effects for events near the passivated surface. These surface events can, in general, be distinguished from events that occur in the germanium crystal bulk by its slower pulse rise time. Unfortunately, the rise-time spectra of bulk and surface events starts to convolve with each other at sub-keV energies. In this work, we propose a novel method based on cross-correlation shape-matching combined with a low-pass filter to constrain the initial parameter estimates of the signal pulse. This improvement at the lowest level leads to a 50% reduction in computation time and refinements in the rise-time resolution, which will, in the end, enhance the overall analysis. To evaluate the performance of the method, we simulate artificial pulses that resembles bulk and surface pulses by using a programmable pulse generator module (pulser). The pulser-generated pulses are then used to examine the pulse behaviours at near-threshold energies, suggesting a roughly 70% background-leakage reduction in the bulk spectrum. Finally, the method is tested on data collected from the TEXONO experiment, where the results are consistent with our observations in pulser and demonstrated the possibility of lowering the analysis threshold by at least 10 eVee. © The Author(s) 2025.
Carbon storage and economic efficiency of fruit-based systems in semi-arid region: a symbiotic approach for sustainable agriculture and climate resilience
(Springer Nature, 2024) Manoj Kumar Singh; Sarwan Kumar Yadav; Bhalendra Singh Rajput; Prashant Sharma
Enhancing our understanding of carbon (C) stock in diverse horticulture and fruit-based agroforestry systems has potential to provide farmers with supplementary advantages in terms of poverty alleviation and livelihood development which can significantly benefit C market initiatives like UN-REDD (reducing emissions from deforestation and forest degradation). Therefore, the current study aimed to assess the biomass accumulation, C storage and economic efficacy of seven agro-ecosystems, namely guava-based agri-horticulture system (AHS), mango-AHS, guava- pure orchard (PO), mango-PO, Indian gooseberry -PO, teak boundary plantation (TBP) and annual cropping system (ACS) under two different landscape positions viz., upland and lowland in the semi-arid region of Vindhyan ranges. The result indicated that mango-AHS accumulated significantly (p < 0.05) higher biomass (26.01 t ha−1) and vegetation C density (13.01 t C ha−1) whereas, soil (35.23 t C ha−1), litter (0.64 t C ha−1), and total C density (46.63 t C ha−1) was maximum under mango-PO closely followed by mango-AHS. The guava-PO system exhibited significantly (p < 0.05) higher C sequestration (2.11 t C ha−1 yr−1), and CO2 abatement (7.76 t CO2 ha−1 yr−1) rate compared to other systems with C credit generation of 129.76 US$ ha−1 year−1. However, mango-AHS was the most lucrative system providing net returns of 4835.48 US$ ha−1 yr−1 and 5.87 benefit–cost ratio. The C credits help in getting farmers an additional income; however, the economic impact of C credit was low (1.16–6.80%) when weighed against the overall economic efficacy of the different systems. Overall, the study concluded that farmers in the region should adopt fruit-based systems, especially agroforestry systems to establish mutually beneficial relationships between mitigation of climate change and livelihood stability. Graphical Abstract: (Figure presented.). © The Author(s) 2024.
CDEX-1 1 kg point-contact germanium detector for low mass dark matter searches
(2013) Ke-Jun Kang; Qian Yue; Yu-Cheng Wu; Jian-Ping Cheng; Yuan-Jing Li; Yang Bai; Yong Bi; Jian-Ping Chang; Nan Chen; Ning Chen; Qing-Hao Chen; Yun-Hua Chen; Yo-Chun Chuang; Zhi Deng; Qiang Du; Hui Gong; Xi-Qing Hao; Qing-Ju He; Xin-Hui Hu; Han-Xiong Huang; Teng-Rui Huang; Hao Jiang; Hau-Bin Li; Jian-Min Li; Jin Li; Jun Li; Xia Li; Xin-Ying Li; Xue-Qian Li; Yu-Lan Li; Heng-Yi Liao; Fong-Kay Lin; Shin-Ted Lin; Shu-Kui Liu; Lan-Chun Lü; Hao Ma; Shao-Ji Mao; Jian-Qiang Qin; Jie Ren; Jing Ren; Xi-Chao Ruan; Man-Bin Shen; Lakhwinder Singh; Manoj Kumar Singh; Arun Kumar Soma; Jian Su; Chang-Jian Tang; Chao-Hsiung Tseng; Ji-Min Wang; Li Wang; Qing Wang; Tsz-King Henry Wong; Shi-Yong Wu; Wei Wu; Hao-Yang Xing; Yin Xu; Tao Xue; Li-Tao Yang; Song-Wei Yang; Nan Yi; Chun-Xu Yu; Hao Yu; Xun-Zhen Yu; Xiong-Hui Zeng; Zhi Zeng; Lan Zhang; Yun-Hua Zhang; Ming-Gang Zhao; Wei Zhao; Su-Ning Zhong; Zu-Ying Zhou; Jing-Jun Zhu; Wei-Bin Zhu; Xue-Zhou Zhu; Zhong-Hua Zhu
The CDEX collaboration has been established for direct detection of light dark matter particles, using ultra-low energy threshold point-contact p-type germanium detectors, in China JinPing underground Laboratory (CJPL). The first 1 kg point-contact germanium detector with a sub-keV energy threshold has been tested in a passive shielding system located in CJPL. The outputs from both the point-contact P+ electrode and the outside N+ electrode make it possible to scan the lower energy range of less than 1 keV and at the same time to detect the higher energy range up to 3 MeV. The outputs from both P+ and N+ electrode may also provide a more powerful method for signal discrimination for dark matter experiment. Some key parameters, including energy resolution, dead time, decay times of internal X-rays, and system stability, have been tested and measured. The results show that the 1 kg point-contact germanium detector, together with its shielding system and electronics, can run smoothly with good performances. This detector system will be deployed for dark matter search experiments. © 2013 Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Sciences and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Changes in the weed seed bank in long-term establishment methods trials under rice-wheat cropping system
(MDPI, 2020) Prashant Sharma; Manoj Kumar Singh; Kamlesh Verma; Saroj Kumar Prasad
The rice–wheat cropping system in the Indo-Gangetic Plains is the backbone of food security in India. In the 1990s, due to the scarcity of resources, the traditional Crop Establishment (CE) method shifted from Conventional Till Puddle Transplanted Rice (CTPTR) to CT Direct Seeded Rice (CTDSR) and Zero-Till DSR (ZTDSR) in paddy; and in wheat, from Conventional Till Wheat (CTW) to Zero Till Wheat (ZTW), with residue retention in rice (RRR) or in both rice and wheat (RRRW). Shift in CE methods led to change in Weed Seed Bank (WSB) dynamics and ultimately affected the weed management program. After five years of field trials, soil samples were drawn as per 2-factors factorial randomized block design. Factor-I comprised 4-CE methods, whereas factor-II consisted of 3-soil depths (0–10, 10–20 and 20–30 cm). Results showed CTPTR-CTW and ZTDSR-ZTW (RRRW) record the highest seed bank (SB) of grasses, sedges and BLWs as total weeds, in general; and predominant weeds like Echinochloa spp., Ammania baccifera, Commelina benghalensis and Digitaria sanguinalis, in particular. It also showed the higher species richness (DMg) and Shannon–Weaver (H’) indices. CTDSR-CTW and CTDSR-ZTW (RRR) show the lowest WSB and at par with Shannon–Weaver (H’) index; further, lowest species richness (DMg) under CTDSR-CTW. Species Evenness (J’) and Simpson index (λ) vary non-significantly with CE methods. Furthermore, 0–10 cm soil depth showed the highest SB of different category of total weed, predominant weeds as well as higher values of DMg, H’, and λ; whereas reverse trend was observed in Whittaker Statistic (βW). Interaction between CE methods and soil depth revealed most of WSB lying on the top layer in case of ZTDSR-ZTW (RRRW) and CTDSR-ZTW (RRR); while CTPTR-CTW showed almost uniform WSB distribution, and in case of CTDSR-CTW, a gradual decrease in WSB with soil depth. © 2020 by the authors.
Characteristic analysis of aspheric quasi-optical lens antenna in millimeter-wave radiometer imaging system
(OSA - The Optical Society, 2013) Won-Gyum Kim; Nam-Won Moon; Manoj Kumar Singh; Hwang-Kyeom Kim; Yong-Hoon Kim
Quasi-optical imaging systems require low blurring effect and large depth of focus (DOF) to get an acceptable sharpness of the image. To reduce aberration-limited blurring, the aspheric convex plano lenses with an aperture diameter of 350 mm are designed in W-band. We analyzed theoretically and experimentally the millimeter-wave band lens characteristics, such as beam spot size, spatial resolution (SR), and DOF, via f-number. It is first used to verify the DOF through f-number in the system-level test with the developed W-band radiometer imaging system. We have confirmed that the larger f-number of quasi-optical lens leads to a larger DOF but a lower SR. © 2013 Optical Society of America.